Download or read book Semantic oriented Object Segmentation written by Wenbin Zou and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis focuses on the problems of object segmentation and semantic segmentation which aim at separating objects from background or assigning a specific semantic label to each pixel in an image. We propose two approaches for the object segmentation and one approach for semantic segmentation. The first proposed approach for object segmentation is based on saliency detection. Motivated by our ultimate goal for object segmentation, a novel saliency detection model is proposed. This model is formulated in the low-rank matrix recovery model by taking the information of image structure derived from bottom-up segmentation as an important constraint. The object segmentation is built in an iterative and mutual optimization framework, which simultaneously performs object segmentation based on the saliency map resulting from saliency detection, and saliency quality boosting based on the segmentation. The optimal saliency map and the final segmentation are achieved after several iterations. The second proposed approach for object segmentation is based on exemplar images. The underlying idea is to transfer segmentation labels of globally and locally similar exemplar images to the query image. For the purpose of finding the most matching exemplars, we propose a novel high-level image representation method called object-oriented descriptor, which captures both global and local information of image. Then, a discriminative predictor is learned online by using the retrieved exemplars. This predictor assigns a probabilistic score of foreground to each region of the query image. After that, the predicted scores are integrated into the segmentation scheme of Markov random field (MRF) energy optimization. Iteratively finding minimum energy of MRF leads the final segmentation. For semantic segmentation, we propose an approach based on region bank and sparse coding. Region bank is a set of regions generated by multi-level segmentations. This is motivated by the observation that some objects might be captured at certain levels in a hierarchical segmentation. For region description, we propose sparse coding method which represents each local feature descriptor with several basic vectors in the learned visual dictionary, and describes all local feature descriptors within a region by a single sparse histogram. With the sparse representation, support vector machine with multiple kernel learning is employed for semantic inference. The proposed approaches have been extensively evaluated on several challenging and widely used datasets. Experiments demonstrated the proposed approaches outperform the stateofthe- art methods. Such as, compared to the best result in the literature, the proposed object segmentation approach based on exemplar images improves the F-score from 63% to 68.7% on Pascal VOC 2011 dataset.

Download or read book Multispectral Image Analysis Using the Object Oriented Paradigm written by Kumar Navulur and published by CRC Press. This book was released on 2006-12-05 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bringing a fresh new perspective to remote sensing, object-based image analysis is a paradigm shift from the traditional pixel-based approach. Featuring various practical examples to provide understanding of this new modus operandi, Multispectral Image Analysis Using the Object-Oriented Paradigm reviews the current image analysis methods and demonstrates advantages to improve information extraction from imagery. This reference describes traditional image analysis techniques, introduces object-oriented technology, and discusses the benefits of object-based versus pixel-based classification. It examines the creation of object primitives using image segmentation approaches and the use of various techniques for object classification. The author covers image enhancement methods, how to use ancillary data to constrain image segmentation, and concepts of semantic grouping of objects. He concludes by addressing accuracy assessment approaches. The accompanying downloadable resources present sample data that enable the use of different approaches to problem solving. Integrating remote sensing techniques and GIS analysis, Multispectral Image Analysis Using the Object-Oriented Paradigm distills new tools to extract information from remotely sensed data.

Download or read book Region oriented Convolutional Networks for Object Retrieval written by Eduard Fontdevila Bosch and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis is framed in the computer vision eld, addressing a challenge related to instance search. Instance search consists in searching for occurrences of a certain visual instance on a large collection of visual content, and generating a ranked list of results sorted according to their relevance to a user query. This thesis builds up on existing work presented at the TRECVID Instance Search Task in 2014, and explores the use of local deep learning features extracted from object proposals. The performance of di erent deep learning architectures (at both global and local scales) is evaluated, and a thorough comparison of them is performed. Secondly, this thesis presents the guidelines to follow in order to ne-tune a convolutional neural network for tasks such as image classi cation, object detection and semantic segmentation. It does so with the nal purpose of ne tuning SDS, a CNN trained for both object detection and semantic segmentation, with the recently released Microsoft COCO dataset.

Download or read book Applications of Deep Learning in Large scale Object Detection and Semantic Segmentation written by Wei Xiang (Ph.D.) and published by . This book was released on 2019 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the massive storage of multimedia data and increasing computational power of mobile devices, developing scalable computer vision applications has become the primary motivation for both research and industrial community. Among these applications, object detection and semantic segmentation are two of the most popular topics which, in addition, serve as the fundamental features for many computer vision systems under platforms like mobile, healthcare, autonomous driving, etc. Inspired by the current and foreseeable trend, this thesis focuses on developing both effective and efficient object detection and semantic segmentation models, with the large-scale,publicly available data sets sourced for various applications.In the last several years, object detection and semantic segmentation have received large attention in the literature, and have been significantly advanced with the emergence of deep learning methods. Particularly, by applying Convolutional Neural Networks (CNNs), researchers have leveraged unsupervised features in modeling which greatly simplified the tasks of classification and regression, compared to using merely hand-crafted features in those traditional approaches. In object detection, however, there still exist many open research problems like integrating contextual information to the existing models, the missing relationship between proposal scales and receptive field sizes for different CNNs, etc. In this thesis,we study extensively such relationship, and further demonstrate that our statistical results can be used as a guideline to design both heuristically and efficiently new detection models, with an improvement of detection accuracy particularly for small objects.In semantic segmentation, we investigate many of the state-of-the-art methods and figure out that current research have largely focused on using complicated backbones together with some popular meta-architectures and designs which, in turn,leads to the problem of overtting and incapability for real-time tasks. To overcome this issue, we propose Turbo Unified Network (ThunderNet), which builds on a minimum backbone followed by a pyramid pooling module and a customized, two-level lightweight decoder. Our experimental results show that ThunderNet remains one of the fastest models that are currently available, while achieving comparable accuracy to a majority of methods in the literature. We also test ThunderNet with a GPU-powered embedded platform{NVIDIA Jetson TX2, whose results indicate that ThunderNet performs sufficiently fast and accurate, thus meeting the demands for embedded system. Finally, this thesis also surveys on the joint calibration methods for RGB-D sensor. We summarize the related work and present our quantitative evaluation results thereafter.

Download or read book Semantic Multimedia written by Bianca Falcidieno and published by Springer Science & Business Media. This book was released on 2007-11-30 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second International Conference on Semantics and Digital Media Technologies, SAMT 2007, held in Genoa, Italy, in December 2007. The conference brings together forums, projects, institutions and individuals investigating the integration of knowledge, semantics and low-level multimedia processing, including new emerging media and application areas. The papers are organized in topical sections.

Download or read book written by Prof. Jun Yeh, Tallinn University of Technology, Estonia and published by DEStech Publications, Inc. This book was released on 2014-07-07 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: amount of new knowledge every day. We have to acknowledge that even the smartest people among us are incapable of familiarizing himself with all these new data. Fortunately, we are only required to deal with a very small amount of that vast number in our work and life. As those who devote himself to the field of information technology and management engineering, I sincerely believe that it is our responsibility to make efforts to accelerate the advance of science in such fields. The 2014 international Conference on Information Technology and Management Engineering, thanks to the hard work of its committee, will be held on April 26 and 27 in Hong Kong. The ITME2014 covers a wide range of topics such as network protocols, information theory and coding theory, network security, management theory, project management, public management, knowledge management etc. It is a great honor to us that numerous people from various countries, including many famous experts and excellent researchers, have shown their interest in this convention and submitted their latest studies to us as their support. Among these studies, we have selected about a hundred to be finally included in this proceeding after reviewing and discussing. We believe that this collection of work will be of great value not only to the participants of ITME2014, but also to those who has a chance of meeting it. The publication of this conference proceedings and the successful opening of ITME2014 owe its credit to a lot of people and institutions, especially the ITME2014 committee, the editors and DEStech Publications. The committee has devoted much time to reviewing the papers submitted to ITME2014, and DEStech Publications publishing those accepted papers. I would like to thank the committee and the press deeply here for their support to ITME2014 and I am eagerly looking forward to another chance for us to be a team again. Finally, let’s wish together that the 2014 International Conference on Information Technology

Download or read book Semantic Segmentation for Random Object Picking Based on Deep Learning written by Chien-Ming Lin and published by . This book was released on 2018 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Database Semantics written by Zahir Tari and published by Springer Science & Business Media. This book was released on 1998-12-31 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: Database Semantics: Semantic Issues in Multimedia Systems reflects the state of the art of emerging research on the meaning of multimedia information, as presented during IFIP's Eighth Data Semantics Working Conference (DS-8), organized by its Working Group 2.6 on Databases, and held at Rotorua, New Zealand, in January 1999. DS-8 was planned as an active forum for researchers and practitioners focusing on those issues that involve the semantics of the information represented, stored, and manipulated by multimedia systems. Depending on the topic and state of research, issues may be covered either deeply theoretically or quite practically, or even both. These proceedings contain twenty-one papers carefully selected by an International Programme Committee and organized in six thematic areas: Video Data Modelling and Use; Image Databases; Applications of Multimedia Systems; Multimedia Modeling in General; Multimedia Information Retrieval; Semantics and Metadata. For almost every area, important topics and issues include: data modeling and query languages for media such as audio, video, and images; methodological aspects of multimedia database design; intelligent multimedia information retrieval; knowledge discovery and data mining in multimedia information; multimedia user interfaces. Three visionary keynote addresses, by famous experts Ramesh Jain, Hermann Maurer and Masao Sakauchi, set the stage for discussion and future directions for the field. The collection of papers that resulted now offers a glimpse of the excitement and enthusiasm from DS-8. Database Semantics: Semantic Issues in Multimedia Systems is suitable as a secondary text for a graduate-level course on database systems, multimedia systems, or information retrieval systems and as a reference for practitioners and researchers in industry.

Download or read book Bridging the Semantic Gap in Image and Video Analysis written by Halina Kwaśnicka and published by Springer. This book was released on 2018-02-20 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents cutting-edge research on various ways to bridge the semantic gap in image and video analysis. The respective chapters address different stages of image processing, revealing that the first step is a future extraction, the second is a segmentation process, the third is object recognition, and the fourth and last involve the semantic interpretation of the image. The semantic gap is a challenging area of research, and describes the difference between low-level features extracted from the image and the high-level semantic meanings that people can derive from the image. The result greatly depends on lower level vision techniques, such as feature selection, segmentation, object recognition, and so on. The use of deep models has freed humans from manually selecting and extracting the set of features. Deep learning does this automatically, developing more abstract features at the successive levels. The book offers a valuable resource for researchers, practitioners, students and professors in Computer Engineering, Computer Science and related fields whose work involves images, video analysis, image interpretation and so on.

Download or read book Semantic and Generic Object Segmentation for Scene Analysis Using RGB D Data written by Xiao Lin and published by . This book was released on 2018 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we study RGB-D based segmentation problems from different perspectives in terms of the input data. Apart from the basic photometric and geometric information contained in the RGB-D data, also semantic and temporal information are usually considered in an RGB-D based segmentation system. The first part of this thesis focuses on an RGB-D based semantic segmentation problem, where the predefined semantics and annotated training data are available. First, we review how RGB-D data has been exploited in the state of the art to help training classifiers in a semantic segmentation tasks. Inspired by these works, we follow a multi-task learning schema, where semantic segmentation and depth estimation are jointly tackled in a Convolutional Neural Network (CNN). Since semantic segmentation and depth estimation are two highly correlated tasks, approaching them jointly can be mutually beneficial. In this case, depth information along with the segmentation annotation in the training data helps better defining the target of the training process of the classifier, instead of feeding the system blindly with an extra input channel. We design a novel hybrid CNN architecture by investigating the common attributes as well as the distinction for depth estimation and semantic segmentation. The proposed architecture is tested and compared with state of the art approaches in different datasets. Although outstanding results are achieved in semantic segmentation, the limitations in these approaches are also obvious. Semantic segmentation strongly relies on predefined semantics and a large amount of annotated data, which may not be available in more general applications. On the other hand, classical image segmentation tackles the segmentation task in a more general way. But classical approaches hardly obtain object level segmentation due to the lack of higher level knowledge. Thus, in the second part of this thesis, we focus on an RGB-D based generic instance segmentation problem where temporal information is available from the RGB-D video while no semantic information is provided. We present a novel generic segmentation approach for 3D point cloud video (stream data) thoroughly exploiting the explicit geometry and temporal correspondences in RGB-D. The proposed approach is validated and compared with state of the art generic segmentation approaches in different datasets. Finally, in the third part of this thesis, we present a method which combines the advantages in both semantic segmentation and generic segmentation, where we discover object instances using the generic approach and model them by learning from the few discovered examples by applying the approach of semantic segmentation. To do so, we employ the one shot learning technique, which performs knowledge transfer from a generally trained model to a specific instance model. The learned instance models generate robust features in distinguishing different instances, which is fed to the generic segmentation approach to perform improved segmentation. The approach is validated with experiments conducted on a carefully selected dataset.

Download or read book High Order Models in Semantic Image Segmentation written by Ismail Ben Ayed and published by Elsevier. This book was released on 2023-06-16 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-Order Models in Semantic Image Segmentation reviews recent developments in optimization-based methods for image segmentation, presenting several geometric and mathematical models that underlie a broad class of recent segmentation techniques. Focusing on impactful algorithms in the computer vision community in the last 10 years, the book includes sections on graph-theoretic and continuous relaxation techniques, which can compute globally optimal solutions for many problems. The book provides a practical and accessible introduction to these state-of -the-art segmentation techniques that is ideal for academics, industry researchers, and graduate students in computer vision, machine learning and medical imaging. Gives an intuitive and conceptual understanding of this mathematically involved subject by using a large number of graphical illustrations Provides the right amount of knowledge to apply sophisticated techniques for a wide range of new applications Contains numerous tables that compare different algorithms, facilitating the appropriate choice of algorithm for the intended application Presents an array of practical applications in computer vision and medical imaging Includes code for many of the algorithms that is available on the book's companion website

Download or read book Advances in Databases and Information Systems written by Barbara Catania and published by Springer Science & Business Media. This book was released on 2010-09-09 with total page 614 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 14th East European Conference on Advances in Databases and Information Systems, ADBIS 2010, held in Novi Sad, Serbia on September 20-24, 2010. The 36 revised full papers and 14 short papers were carefully selected from 165 submissions. Tolically the papers span a wide spectrum of topics in the database and information systems field, including database theory, advanced DBMS technologies, design methods, data mining and data warehousing, spatio-temporal and graph structured data and database applications.

Download or read book Computer Vision ECCV 2014 written by David Fleet and published by Springer. This book was released on 2014-08-14 with total page 855 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed proceedings of the 13th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014. The 363 revised papers presented were carefully reviewed and selected from 1444 submissions. The papers are organized in topical sections on tracking and activity recognition; recognition; learning and inference; structure from motion and feature matching; computational photography and low-level vision; vision; segmentation and saliency; context and 3D scenes; motion and 3D scene analysis; and poster sessions.

Download or read book Semantic Segmentation by Deep Convolutional Network written by Xiaoxiao Li (Ph.D. in information engineering) and published by . This book was released on 2018 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object segmentation is a fundamental and long-standing problem in computer vision. It is defined as a multi-label classification problem, aiming to part ition different objects into different segments. According to the different types of inputs and goals, this task can be further divided into sub-tasks, i.e., semantic image segmentation , video object segmentation and so on . This thesis mainly focuses on addressing the segmentation t ask by using deep learning techniques, include increasing accuracy and accelerating the algorithm. By studying two typical segmentation t asks, we propose three novel approaches to address the common challenges in object segmentation.

Download or read book Semantic Models for Multimedia Database Searching and Browsing written by Shu-Ching Chen and published by Springer Science & Business Media. This book was released on 2005-12-08 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: Semantic Models for Multimedia Database Searching and Browsing begins with the introduction of multimedia information applications, the need for the development of the multimedia database management systems (MDBMSs), and the important issues and challenges of multimedia systems. The temporal relations, the spatial relations, the spatio-temporal relations, and several semantic models for multimedia information systems are also introduced. In addition, this book discusses recent advances in multimedia database searching and multimedia database browsing. More specifically, issues such as image/video segmentation, motion detection, object tracking, object recognition, knowledge-based event modeling, content-based retrieval, and key frame selections are presented for the first time in a single book. Two case studies consisting of two semantic models are included in the book to illustrate how to use semantic models to design multimedia information systems. Semantic Models for Multimedia Database Searching and Browsing is an excellent reference and can be used in advanced level courses for researchers, scientists, industry professionals, software engineers, students, and general readers who are interested in the issues, challenges, and ideas underlying the current practice of multimedia presentation, multimedia database searching, and multimedia browsing in multimedia information systems.

Download or read book Inter deriving Semantic Artifacts for Object oriented Programming written by Olivier Danvy and published by . This book was released on 2008 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Computer Vision ECCV 2014 written by David Fleet and published by Springer. This book was released on 2014-09-22 with total page 632 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed proceedings of the 13th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014. The 363 revised papers presented were carefully reviewed and selected from 1444 submissions. The papers are organized in topical sections on tracking and activity recognition; recognition; learning and inference; structure from motion and feature matching; computational photography and low-level vision; vision; segmentation and saliency; context and 3D scenes; motion and 3D scene analysis; and poster sessions.